Liver boundary identification method and system

ABSTRACT

The present disclosure relates to the technical field of medical image processing and, in particular, to a liver boundary identification method and a system. The method includes: obtaining liver tissue information of a liver tissue to be identified; identifying a liver tissue boundary in the liver tissue information according to a feature of the liver tissue corresponding to the liver tissue information and a feature of the liver tissue boundary corresponding to the liver tissue information using an image processing technology or a signal processing technology; and outputting position information of the identified liver tissue boundary. By using the disclosed method, the liver tissue boundary can be identified automatically, the efficiency of identifying the liver boundary can be improved, and automatic positioning of the liver boundary can thus be achieved.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No. 15/487,032, filed on Apr. 13, 2017, which is a continuation of International Application No. PCT/CN2015/081838, filed on Aug. 10, 2015. The International Application claims priority to Chinese Patent Application No. 201410564295.5, filed on Oct. 21, 2014. All of the afore-mentioned patent applications are hereby incorporated by reference in their entireties.

TECHNICAL FIELD

The present disclosure relates to the technical field of medical image processing and, in particular, to a liver boundary identification method and a system.

BACKGROUND

Many clinical applications require identifying a liver tissue boundary from conventional medical imaging including ultrasonic imaging, magnetic resonance imaging (MRI), computed tomography (CT) and so on, so as to locate a liver examination region, such as for liver elasticity examination and liver color Doppler ultrasonography etc.

At present, the liver tissue boundary is mostly identified manually. However, selecting the liver boundary manually according to liver tissue information requires the operator to be highly familiar with the liver tissue structure and image information so that the liver tissue boundary can be selected accurately, which is very demanding for the operator. Meanwhile, the relatively long time it takes to manually conduct the identification in the identification process can lead to low efficiency in identifying the liver boundary.

SUMMARY

An objective of the present disclosure lies in proposing a liver boundary identification method and a system to improve the efficiency of liver boundary identification.

On an aspect, the present disclosure provides a liver boundary identification method, including:

obtaining liver tissue information of a liver tissue to be identified;

identifying a liver tissue boundary in the liver tissue information according to a feature of the liver tissue corresponding to the liver tissue information and a feature of the liver tissue boundary corresponding to the liver tissue information using an image processing technology or a signal processing technology; and

outputting position information of the identified liver tissue boundary.

On another aspect, the present disclosure provides a liver boundary identification system, including an information obtaining device, a liver boundary identifying device, and a liver boundary displaying device, where the information obtaining device is configured to obtain liver tissue information of a liver tissue to be identified; the liver boundary identifying device is configured to identify a liver tissue boundary in the liver tissue information according to a feature of the liver tissue corresponding to the liver tissue information and a feature of the liver tissue boundary corresponding to the liver tissue information using an image processing technology or a signal processing technology; and the liver boundary displaying device is configured to output position information of the identified liver tissue boundary.

The liver boundary identification method and the system provided in embodiments of the present disclosure can identify the liver boundary region efficiently. According to the liver boundary identification method provided in embodiments of the present disclosure, the liver tissue information of the liver tissue to be identified is obtained, and then the image processing technology or signal processing technology is employed to identify the liver tissue boundary according to signal features of the liver tissue and its boundary. By using the disclosed method, the liver tissue boundary can be identified automatically, and the efficiency of identifying the liver tissue boundary can be improved.

BRIEF DESCRIPTION OF DRAWINGS

The figures described herein are intended to provide further understanding about embodiments of the present disclosure, and constitute a part of, rather than a limitation on, the embodiments of the present disclosure. In the figures:

FIG. 1 is a flowchart implementing a liver boundary identification method provided in a first embodiment of the present disclosure;

FIG. 2 is a flowchart implementing a liver boundary identification method provided in a second embodiment of the present disclosure;

FIG. 3 is a schematic view illustrating an effect of boundary identification based on an M-type ultrasonic signal of a liver tissue in the second embodiment of the present disclosure;

FIG. 4 is a flowchart implementing a liver boundary identification method provided in a third embodiment of the present disclosure;

FIG. 5 is a schematic view illustrating an effect of boundary identification based on a B-type ultrasonic image of a liver tissue in the third embodiment of the present disclosure;

FIG. 6 is a flowchart implementing a liver boundary identification method provided in a fourth embodiment of the present disclosure;

FIG. 7 is a schematic view illustrating an effect of boundary identification based on a CT image of a liver tissue in the fourth embodiment of the present disclosure; and

FIG. 8 is a schematic structural view illustrating a liver boundary identification system provided in a fifth embodiment of the present disclosure.

DETAILED DESCRIPTION

Now, embodiments of the present disclosure will be more fully and comprehensively described in combination with the accompanying drawings and particular embodiments. It will be appreciated that the particular embodiments described herein are merely used for explaining, rather than limiting, embodiments of the present disclosure. In addition, it should be further noted that in order to facilitate the description, the drawings only depict portions relevant to the embodiments of the present disclosure, rather than the whole content.

First Embodiment

FIG. 1 is a flowchart implementing a liver boundary identification method provided in a first embodiment of the present disclosure. The method can be performed by a liver boundary identification system. As depicted in FIG. 1, the implementation process includes:

Step 11: obtain liver tissue information of a liver tissue to be identified.

The liver tissue information may be an A-type ultrasonic signal of the liver tissue, an M-type ultrasonic signal of the liver tissue, a B-type ultrasonic image of the liver tissue, a CT image of the liver tissue, or an MRI image of the liver tissue.

According to the liver tissue information, the type of the liver tissue information can be obtained. That is, the type of the liver tissue information may be an ultrasonic signal, e.g. an A-type ultrasonic signal of the liver tissue and an M-type ultrasonic signal of the liver tissue, or a two-dimensional ultrasonic image, e.g. a B-type ultrasonic image of the liver tissue, or a three-dimensional image, e.g. a CT image of the liver tissue and an MRI image of the liver tissue.

Step 12: identify a liver tissue boundary in the liver tissue information according to a feature of the liver tissue corresponding to the liver tissue information and a feature of the liver tissue boundary corresponding to the liver tissue information using an image processing technology or a signal processing technology.

If the liver tissue information is an ultrasonic signal of the liver tissue, use the signal processing technology to identify the liver tissue boundary based on the features of the liver tissue and the liver tissue boundary corresponding to the ultrasonic signal of the liver tissue; if the liver tissue information is a two-dimensional ultrasonic image or a three-dimensional image, use the image processing technology to identify the liver tissue boundary based on the features of the liver tissue and the liver tissue boundary corresponding to the two-dimensional ultrasonic image or three-dimensional ultrasonic image of the liver tissue.

When the liver tissue information is a one-dimensional ultrasonic signal of the liver tissue, a two-dimensional ultrasonic image of the liver tissue or a three-dimensional ultrasonic image of the liver tissue, identifying the liver tissue boundary in the liver tissue information according to the feature of the liver tissue corresponding to the liver tissue information and the feature of the liver tissue boundary corresponding to the liver tissue information using the image processing technology or the signal processing technology may particularly include: partitioning the liver tissue information into a plurality of detection sub-regions; calculating a feature value of the liver tissue information in each detection sub-region; and determining the liver tissue boundary according to the feature values of the liver tissue information in the detection sub-regions.

Step 13: output position information of the identified liver tissue boundary.

Outputting position information of the identified liver tissue boundary may include: outputting a coordinate position of the identified liver tissue boundary; and/or displaying an image of the identified liver tissue boundary. That is, only the coordinate position of the identified liver tissue boundary may be outputted, or only the image of the identified liver tissue boundary may be displayed, or, not only may the coordinate position of the identified liver tissue boundary be outputted, but the image of the identified liver tissue boundary may also be displayed, when outputting the position information of the identified liver tissue boundary.

According to the liver tissue boundary identification method provided in the first embodiment of the present disclosure, the liver tissue boundary can be identified automatically and efficiently according to features of the liver tissue and the liver tissue boundary corresponding to the liver tissue information, where the liver tissue information may be an A-type ultrasonic signal that reflects one-dimensional structural information of the liver tissue or an M-type ultrasonic signal that reflects one-dimensional structural dynamic information of the tissue, or a B-type ultrasonic image that reflects a two-dimensional structure of the liver tissue, or a CT or MRI image that reflects a three-dimensional structure of the liver tissue.

Second Embodiment

The second embodiment is a particular optimization directed towards the Step 12 in the first embodiment of the present disclosure. The method is applicable to a one-dimensional ultrasonic signal of the liver tissue. FIG. 2 is a flowchart implementing a liver boundary identification method provided in a second embodiment of the present disclosure, and FIG. 3 is a schematic view illustrating an effect of boundary identification based on an M-type ultrasonic signal of the liver tissue in the second embodiment of the present disclosure. Combined with FIGS. 2 and 3, the method includes:

Step 21: partition the one-dimensional ultrasonic signal of the liver tissue into a plurality of detection sub-regions S_(i).

The one-dimensional ultrasonic signal of the liver tissue may be an A-type ultrasonic signal of the liver tissue or an M-type ultrasonic signal of the liver tissue. Assuming that one ultrasonic signal contains n sampling points and corresponding scanning depth of the ultrasonic signal of the liver tissue is d (in mm), it can be known that each 1 mm depth contains n/d points. The n sampling points are partitioned into multiple detection sub-regions S_(i), with the detection sub-region S_(i) corresponding to the scanning depth of d_(i), where i is an integer, the scanning depth d_(i) may be an average value of depth or an end value of depth of the detection sub-region S_(i), and the following will be based on the case of end value.

For instance, n sampling points are partitioned into multiple detection sub-regions S_(i) with spacing z. The information about the very bottom of an image produced from ultrasonic imaging (which corresponds to the deepest portion of the scanning depth) may be ignored because the very bottom of the image typically does not contain any detection target. In this case, i=1, 2, . . . , [d/z]−1, where z is the section length of a detection sub-region (in mm), and [ ] indicates rounding up to an integer. In this case, each segment of the detection sub-region contains [zn/d] sampling points, respectively. For example, when the scanning depth d of the ultrasonic signal is 20 mm and the spacing z is 3 mm, n sampling points are partitioned into [d/z]−1=6 detection sub-regions S₁˜S₆, where S₁ corresponds to the section of 0˜3 mm, S₂ corresponds to the section of 3˜6 mm, . . . , S₆ corresponds to the section of 15˜18 mm, and the very bottom of the image (which corresponds to the section of 18˜20 mm) is ignored because it typically does not contain any testing target.

Step 22: calculate a Nakagami distribution value m_(i) for the one-dimensional ultrasonic signal R_(i) of the liver tissue in each detection sub-region S_(i).

The Nakagami statistic model is a type of ultrasonic tissue characterization technology. Particularly, the Nakagami distribution value m_(i) is calculated for the ultrasonic signal R_(i) corresponding to the image of the liver tissue in each detection sub-region S_(i) according to the following formula:

$m_{i} = \frac{\left\lbrack {E\left( R_{i}^{2} \right)} \right\rbrack^{2}}{{E\left\lbrack {R_{i}^{2} - {E\left( R_{i}^{2} \right)}} \right\rbrack}^{2}}$

where the probability density function of the Nakagami distribution is:

${f(r)} = {\frac{2m^{m}r^{{2m} - 1}}{{\Gamma (m)}\Omega^{m}}{\exp \left( {{- \frac{m}{\Omega}}r^{2}} \right)}{U(r)}}$

where E(.) is a mean value function, Γ(.) indicates a gamma function, Ω=E(r²), U(.) indicates a unit step function, m is the Nakagami distribution value, r is a dependent variable of a probability distribution function f(r), r≥0, m≥0; and for each detection sub-region S_(i), m_(i) is the m value in the region S_(i), and R_(i) is an envelope value of the ultrasonic signal.

When the m value falls in the range of (0, 1), the ultrasonic signal of liver tissue follows pre-Rayleigh distribution. When the m value equals to 1, the ultrasonic echo-signal follows Rayleigh distribution. When the m value is larger than 1, the ultrasonic echo-signal follows post-Rayleigh distribution.

Step 23: calculate a weight W_(i) for each detection sub-region S_(i) according to the following formula, and determine the detection sub-region corresponding to the maximum weight value to be a boundary region of the liver tissue:

$W_{i} = \frac{100*m_{i}}{\sqrt{d_{i}}}$

where d_(i) is the scanning depth corresponding to the detection sub-region S_(i), and may also be an average value of depth or an end value of depth of the detection sub-region S_(i). The weights W_(i) of respective detection sub-regions are traversed through to pick out the detection sub-region corresponding to the maximum weight value to be a boundary region of the liver tissue, thus accomplishing automatic positioning of the liver tissue boundary.

The liver boundary identification method provided in the second embodiment of the present disclosure is capable of realizing real-time automatic positioning of the liver tissue boundary via A-type or M-type ultrasonic signals of the liver tissue. Additionally, this algorithm has a low complexity level, which can offer higher efficiency in identifying the liver tissue boundary, thereby enabling real-time automatic positioning of the liver tissue boundary.

Third Embodiment

The third embodiment is a particular optimization directed towards the Step 12 in the first embodiment of the present disclosure The method is applicable to a two-dimensional ultrasonic signal of the liver tissue. FIG. 4 is a flowchart implementing a liver boundary identification method provided in a third embodiment of the present disclosure, and FIG. 5 is a schematic view illustrating an effect of boundary identification based on a B-type ultrasonic image of the liver tissue in the third embodiment of the present disclosure. Combined with FIGS. 4 and 5, the method includes:

Step 31: partition the two-dimensional ultrasonic image of the liver tissue into a plurality of rectangular detection sub-regions R_(ij), where i and j are natural numbers that indicate the row index and column index of respective detection sub-region, respectively.

The two-dimensional ultrasonic image of the liver tissue may be a B-type ultrasonic image of liver tissue. Assuming that a B-type ultrasonic image has a dimension of w*h, where w is the width of the two-dimensional ultrasonic image of the liver tissue, h is the height of the two-dimensional ultrasonic image of the liver tissue (both w and h are measured by pixel), and the corresponding scanning depth is d (in mm), it can be known that each 1 mm depth along a scanning line in the depth direction contains h/d pixel points. The B-type ultrasonic image of dimension w*h is partitioned into a plurality of rectangular detection sub-regions R_(ij).

For example, the B-type ultrasonic image of dimension w*h is partitioned into multiple square detection sub-regions R_(ij) with side length z. Similar to the first embodiment, the information about the very bottom of the image produced from ultrasonic imaging (which corresponds to the deepest portion of the scanning depth) and the edges thereof along the width direction may be ignored because the very bottom and the edges typically do not contain any testing target, where i=1, 2, . . . ,

${\left\lbrack \frac{d}{z} \right\rbrack - 1},$

j=1, 2, . . . ,

${\left\lbrack \frac{wd}{hz} \right\rbrack - 1},$

z is the length of a side of a square detection sub-region (in mm), and [ ] indicates rounding up to an integer. In this case, the width and height of each square detection sub-region R_(ij) are both [zh/d] pixels.

Step 32: calculate a weight W_(ij) of each detection sub-region R_(ij), and determine the detection sub-region corresponding to the maximum weight value to be a boundary region of the liver tissue. In order to reduce the amount of calculation, only weights of half of the detection sub-regions can be calculated. For instance, the two-dimensional ultrasonic image may be split into two portions along a center line, and only the weights W_(kj) (k=i_(max)/2) of the detection sub-regions R_(kj) in the upper half of the two-dimensional ultrasonic image are calculated to find out the boundary sub-region above the center line. The complete boundary region can then be obtained by extending the boundary sub-region along the width direction (i.e. lateral direction). In this case, the weight W_(kj) can be calculated according to the following formula:

$W_{kj} = \frac{M_{kj}}{{SD}_{kj}*\sqrt{d_{kj}}}$

where M_(kj) is an average gray value of the two-dimensional ultrasonic image of the liver tissue in the detection sub-region R_(kj), SD_(kj) is standard deviation of grayscale of the two-dimensional ultrasonic image of the liver tissue in the detection sub-region R_(kj), and d_(kj) is scanning depth corresponding to the detection sub-region R_(kj). As can be known from k=i_(max)/2,

$k = {\frac{1}{2}\left\lbrack {\left\lbrack \frac{wd}{hz} \right\rbrack - 1} \right\rbrack}$

and k is an integer when the two-dimensional ultrasonic image of the liver tissue is partitioned into rectangular regions of side length z, where i_(max) is the maximum value in the value range of i.

The liver boundary region has a higher average gray value because the envelope region of liver is presented on a B-type ultrasonic image as uniform high-level echo. Additionally, the standard deviation of the grayscale is relatively low because the envelope region of liver appears uniform on the B-type ultrasonic image. In order to avoid the black background regions on two sides of the fan-shaped B-type ultrasonic image produced from the convex array probe scanning, the search will begin from the detection sub-region at the center line of the B-type ultrasonic image, and if the detection sub-region R_(k1) is the one with the largest weight among a series of detection regions R_(k1), the detection sub-region R_(k1) is determined to be the liver tissue boundary.

The liver boundary identification method provided in the third embodiment is capable of realizing real-time automatic positioning of the liver tissue boundary via the B-type ultrasonic image of liver tissue. This algorithm has a low complexity level, which can offer higher efficiency when identifying the liver tissue boundary, thereby enabling real-time automatic positioning of the liver tissue boundary.

Fourth Embodiment

The fourth embodiment is a particular optimization directed towards the Step 12 in the first embodiment of the present disclosure. The method is applicable to a three-dimensional ultrasonic signal of the liver tissue. FIG. 6 is a flowchart implementing a liver boundary identification method provided in a fourth embodiment of the present disclosure, and FIG. 7 is a schematic view illustrating an effect of boundary identification based on a CT image liver tissue in the fourth embodiment of the present disclosure. Combined with FIGS. 6 and 7, the method includes:

Step 41: extract, from the CT image of the liver tissue or the MRI image of the liver tissue, a binary image of skin and a binary image of bones using an image segmentation method.

The process begins from extracting a binary image of skin. The binary image of skin is extracted by using an image segmentation method (e.g. a region growing segmentation method) and taking the pixel of image coordinates (0, 0) as the seed point, where the region growing criterion corresponding to the CT value of air is [−1024, −500] HU (Hounsfield unit).

Then, the process proceeds to extracting a binary image of bones, including a binary image of the spine and a binary image of the rib. Threshold segmentation is performed for the whole image based on a threshold range of [350, 1024] HU to extract the binary image of bones.

Step 42: calculate a center of mass of the binary image of bones, and calculate a point on the binary image of skin that is nearest to the center of mass.

Calculate the center of mass P_(C) of the binary image of bones. The center of mass of the spine P_(C) is taken as the center of mass of the binary image of bones because the ribs are typically distributed symmetrically on the left and right hand side of the spine, and the spine takes up a relatively large proportion in the bone image.

Taking the center of mass P_(C) of the spine as the starting point, search for the point on the binary image of skin that is nearest to the center of mass P_(C) and denoted as P_(N).

Step 43: partition the image of the liver tissue into four quadrants according to the center of mass and the point nearest to the center of mass.

In partitioning the image of the liver tissue into four quadrants by utilizing the center of mass P_(C) and the point P_(N) nearest to the center of mass, a straight line on which the center of mass P_(C) and the point P_(N) nearest to the center of mass are located is taken as the vertical axis, and a straight line passing through the center of mass P_(C) and perpendicular to the vertical axis is taken as the horizontal axis. Most parts of the liver are located in the second quadrant.

Step 44: fit respective rib points in the second quadrant to obtain a rib fitted curve.

The rib fitted curve is obtained by fitting respective rib points in the second quadrant with a B spline or a skin curve.

Step 45: move the rib fitted curve towards a first quadrant by a predefined value to obtain a boundary curve, and determine a region between the boundary curve and the rib fitted curve to be a boundary region of the liver tissue.

Because the rib curve is close to the envelope of liver, the rib curve is moved inwards by a predefined value and then taken as the boundary curve, and the region between the boundary curve and the rib fitted curve is determined to be the boundary region of the liver tissue.

The predefined value may be 5 mm.

The liver boundary identification method provided in the fourth embodiment of the present disclosure is capable of realizing real-time automatic positioning of the liver tissue boundary via the CT image or MRI image of the liver tissue. Additionally, this algorithm has a low complexity level, which can offer higher efficiency in identifying the liver tissue boundary, thereby enabling real-time automatic positioning of the liver tissue boundary.

Fifth Embodiment

FIG. 8 is a schematic structural view illustrating a liver boundary identification system provided in a fifth embodiment of the present disclosure. As depicted in FIG. 8, a liver boundary identification system described in this embodiment may include an information obtaining device 51, a liver boundary identifying device 52, and a liver boundary displaying device 53, where the information obtaining device 51 is configured to obtain liver tissue information of a liver tissue to be identified; the liver boundary identifying device 52 is configured to identify a liver tissue boundary in the liver tissue information according to a feature of the liver tissue corresponding to the liver tissue information and a feature of the liver tissue boundary corresponding to the liver tissue information using an image processing technology or a signal processing technology; and the liver boundary displaying device 53 is configured to output position information of the identified liver tissue boundary.

If the liver tissue information is a one-dimensional ultrasonic signal of the liver tissue, a two-dimensional ultrasonic image of the liver tissue or a three-dimensional ultrasonic image of the liver tissue, the liver boundary identifying device 52 may include: a region partitioning unit, configured to partition the liver tissue information into a plurality of detection sub-regions; and a boundary determining unit, configured to: calculate a feature value of the liver tissue information in each of the detection sub-regions, and determine the liver tissue boundary according to the feature values of the liver tissue information in the detection sub-regions.

If the liver tissue information is a one-dimensional ultrasonic signal of the liver tissue, the boundary determining unit may include: a first feature value calculating sub-unit, configured to calculate a Nakagami distribution value m_(i) for a one-dimensional ultrasonic signal R_(i) of the liver tissue in each detection sub-region S_(i); and a first boundary determining sub-unit, configured to: calculate a weight W_(i) of each detection sub-region S_(i) according to the following formula, and determine the detection sub-region corresponding to the maximum weight value to be a boundary region of the liver tissue:

$W_{i} = \frac{100*m_{i}}{\sqrt{d_{i}}}$

where d_(i) is scanning depth corresponding to the detection sub-region S_(i).

If the liver tissue information is a two-dimensional ultrasonic image of the liver tissue, the region partitioning unit is specifically configured to partition the two-dimensional ultrasonic image of the liver tissue into a plurality of rectangular detection sub-regions R_(ij); the boundary determining unit may be specifically configured to: calculate a weight W_(kj) of each detection sub-region R_(kj) according to the following formula, and determine the detection sub-region R_(ij) corresponding to the maximum weight value to be a boundary region of the liver tissue:

$W_{kj} = \frac{M_{kj}}{{SD}_{kj}*\sqrt{d_{kj}}}$

where M_(kj) is an average gray value of the two-dimensional ultrasonic image of the liver tissue in the detection sub-region R_(kj), SD_(kj) is standard deviation of grayscale of the two-dimensional ultrasonic image of the liver tissue in the detection sub-region R_(kj), d_(kj) is scanning depth corresponding to the detection sub-region R_(kj), and k=i_(max)/2.

If the liver tissue information is a CT image of the liver tissue or an MRI image of the liver tissue, the liver boundary identifying device 52 may specifically include: a binary image obtaining unit, configured to extract, from the CT image of the liver tissue or the MRI image of the liver tissue, a binary image of skin and a binary image of bones using an image segmentation method; a feature point determining unit, configured to: calculate a center of mass of the binary image of bones; and calculate a point on the binary image of skin which is nearest to the center of mass; an image partitioning unit, configured to partition the image of the liver tissue into four quadrants according to the center of mass and the point nearest to the center of mass; a curve fitting unit, configured to fit each rib point in a second quadrant to obtain a rib fitted curve; and a boundary region determining unit, configured to: move the rib fitted curve towards a first quadrant by a predefined value to obtain a boundary region curve; and determine a region between the boundary region curve and the rib fitted curve to be a boundary region of the liver tissue.

According to the liver boundary identification system provided in the fifth embodiment of the present disclosure, the liver tissue boundary can be identified automatically and efficiently according to the feature of the liver tissue boundary corresponding to the liver tissue information, where the liver tissue information may be an A-type ultrasonic signal that reflects one-dimensional structural information of the liver tissue, or an M-type ultrasonic image or an M-type ultrasonic signal that reflects one-dimensional structural dynamic information of the tissue, or a B-type ultrasonic image that reflects a two-dimensional structure of the tissue, or a CT or MRI image that reflects a three-dimensional structure of the liver tissue.

The above described are merely preferred embodiments of, rather than limitations on, embodiments of the present disclosure. For those of ordinary skill in the art, various alterations and changes are possible for embodiments of the present disclosure. Any and all modifications, equivalent replacements, improvements and/or the like made within the spirit and principal of embodiments of the present disclosure should fall within the protection scope of embodiments of the present disclosure. 

What is claimed is:
 1. A liver boundary identification method, comprising: obtaining liver tissue information of a liver tissue to be identified; identifying a liver tissue boundary in the liver tissue information in real-time and automatically, according to a feature of the liver tissue corresponding to the liver tissue information and a feature of the liver tissue boundary corresponding to the liver tissue information using an image processing technology or a signal processing technology; and outputting position information of the identified liver tissue boundary.
 2. The method according to claim 1, wherein, when the liver tissue information is a one-dimensional ultrasonic signal of the liver tissue which comprises an A-type ultrasonic signal of the liver tissue or an M-type ultrasonic signal of the liver tissue, a two-dimensional ultrasonic image of the liver tissue which comprises a B-type ultrasonic image of the liver tissue or a three-dimensional ultrasonic image of the liver tissue which is a CT image of the liver tissue or an MRI image of the liver tissue, identifying a liver tissue boundary in the liver tissue information according to a feature of the liver tissue corresponding to the liver tissue information and a feature of the liver tissue boundary corresponding to the liver tissue information using an image processing technology or a signal processing technology comprises: partitioning the liver tissue information into a plurality of detection sub-regions; and calculating a feature value of the liver tissue information in each of the detection sub-regions, and determining the liver tissue boundary according to the feature value of the liver tissue information in the detection sub-regions.
 3. The method according to claim 2, wherein, when the liver tissue information is a one-dimensional ultrasonic signal of the liver tissue, calculating a feature value of the liver tissue information in each of the detection sub-regions, and determining the liver tissue boundary according to the feature value of the liver tissue information in the detection sub-regions comprises: calculating a Nakagami distribution value m_(i) for a one-dimensional ultrasonic signal R_(i) of the liver tissue in each detection sub-region S_(i); and calculating a weight W_(i) of each detection sub-region S_(i) according to the following formula, and determining the detection sub-region corresponding to a maximum weight value to be a boundary region of the liver tissue: $W_{i} = \frac{100*m_{i}}{\sqrt{d_{i}}}$ where d_(i) is scanning depth corresponding to the detection sub-region S_(i), and i is a natural number.
 4. The method according to claim 2, wherein, when the liver tissue information is a two-dimensional ultrasonic image of the liver tissue, partitioning the liver tissue information into a plurality of detection sub-regions comprises: partitioning the two-dimensional ultrasonic image of the liver tissue into a plurality of rectangular detection sub-regions R_(ij), i and j being natural numbers; calculating a feature value of the liver tissue information in each of the detection sub-regions, and determining the liver tissue boundary according to the feature value of the liver tissue information in the detection sub-regions comprises: calculating a weight W_(kj) of each detection sub-region R_(kj) according to the following formula, and determining the detection sub-region corresponding to a maximum weight value to be a boundary region of the liver tissue: $W_{kj} = \frac{M_{kj}}{{SD}_{kj}*\sqrt{d_{kj}}}$ where M_(kj) is an average gray value of the two-dimensional ultrasonic image of the liver tissue in the detection sub-region R_(kj), SD_(kj) is standard deviation of grayscale of the two-dimensional ultrasonic image of the liver tissue in the detection sub-region R_(kj), d_(kj) is scanning depth corresponding to the detection sub-region R_(kj), k=i_(max)/2 and is a natural number, and i_(max) is a maximum value in a value range of i.
 5. The method according to claim 1, wherein, when the liver tissue information is the CT image of the liver tissue or the MRI image of the liver tissue, identifying a liver tissue boundary in the liver tissue information according to a feature of the liver tissue corresponding to the liver tissue information and a feature of the liver tissue boundary corresponding to the liver tissue information using an image processing technology or a signal processing technology comprises: extracting, from the CT image of the liver tissue or the MRI image of the liver tissue, a binary image of skin and a binary image of bones using an image segmentation method; calculating a center of mass of the binary image of bones, and calculating a point on the binary image of skin which is nearest to the center of mass; partitioning the CT image of the liver tissue or the MRI image of the liver tissue into four quadrants according to the center of mass and the point nearest to the center of mass; fitting each rib point in a second quadrant to obtain a rib fitted curve; and moving the rib fitted curve towards a first quadrant by a predefined value to obtain a boundary curve, and determining a region between the boundary curve and the rib fitted curve to be a boundary region of the liver tissue.
 6. The method according to claim 1, wherein outputting position information of the identified liver tissue boundary comprises: outputting a coordinate position of the identified liver tissue boundary; and/or displaying an image of the identified liver tissue boundary.
 7. The method according to claim 2, wherein outputting position information of the identified liver tissue boundary comprises: outputting a coordinate position of the identified liver tissue boundary; and/or displaying an image of the identified liver tissue boundary.
 8. The method according to claim 3, wherein outputting position information of the identified liver tissue boundary comprises: outputting a coordinate position of the identified liver tissue boundary; and/or displaying an image of the identified liver tissue boundary.
 9. The method according to claim 4, wherein outputting position information of the identified liver tissue boundary comprises: outputting a coordinate position of the identified liver tissue boundary; and/or displaying an image of the identified liver tissue boundary.
 10. The method according to claim 5, wherein outputting position information of the identified liver tissue boundary comprises: outputting a coordinate position of the identified liver tissue boundary; and/or displaying an image of the identified liver tissue boundary.
 11. A liver boundary identification system, comprising a processor and a memory having computer instructions stored therein, the processor, when executing the instructions, is configured to: obtain liver tissue information of a liver tissue to be identified; identify a liver tissue boundary in the liver tissue information in real-time and automatically, according to a feature of the liver tissue corresponding to the liver tissue information and a feature of the liver tissue boundary corresponding to the liver tissue information using an image processing technology or a signal processing technology; and output position information of the identified liver tissue boundary.
 12. The system according to claim 11, wherein when the liver tissue information is a one-dimensional ultrasonic signal of the liver tissue which comprises an A-type ultrasonic signal of the liver tissue or an M-type ultrasonic signal of the liver tissue, a two-dimensional ultrasonic image of the liver tissue which comprises a B-type ultrasonic image of the liver tissue or a three-dimensional ultrasonic image of the liver tissue which is a CT image of the liver tissue or an MRI image of the liver tissue, the processor is further configured to: partition the liver tissue information into a plurality of detection sub-regions; and calculate a feature value of the liver tissue information in each of the detection sub-regions, and determine the liver tissue boundary according to the feature value of the liver tissue information in the detection sub-regions.
 13. The system according to claim 12, wherein when the liver tissue information is a one-dimensional ultrasonic signal of the liver tissue, the processor is further configured to: calculate a Nakagami distribution value m_(i) for a one-dimensional ultrasonic signal R_(i) of the liver tissue in each detection sub-region S_(i); and calculate a weight W_(i) of each detection sub-region S_(i) according to the following formula, and determine the detection sub-region corresponding to a maximum weight value to be a boundary region of the liver tissue: $W_{i} = \frac{100*m_{i}}{\sqrt{d_{i}}}$ where d_(i) is scanning depth corresponding to the detection sub-region S_(i), and i is a natural number.
 14. The system according to claim 12, wherein when the liver tissue information is a two-dimensional ultrasonic image of the liver tissue, the processor is further configured to: partition the two-dimensional ultrasonic image of the liver tissue into a plurality of rectangular detection sub-regions R_(ij), i and j being natural numbers; and calculate a weight W_(kj) of each detection sub-region R_(kj) according to the following formula, and determine the detection sub-region corresponding to a maximum weight value to be a boundary region of the liver tissue: $W_{kj} = \frac{M_{kj}}{{SD}_{kj}*\sqrt{d_{kj}}}$ where M_(kj) is an average gray value of the two-dimensional ultrasonic image of the liver tissue in the detection sub-region R_(kj), SD_(kj) is standard deviation of grayscale of the two-dimensional ultrasonic image of the liver tissue in the detection sub-region R_(kj), d_(kj) is scanning depth corresponding to the detection sub-region R_(kj), k=i_(max)/2 and is a natural number, and i_(max) is a maximum value in a value range of i.
 15. The system according to claim 11, wherein when the liver tissue information is a CT image of the liver tissue or an MRI image of the liver tissue, the processor is further configured to: extract, from the CT image of the liver tissue or the MRI image of the liver tissue, a binary image of skin and a binary image of bones using an image segmentation method; calculate a center of mass of the binary image of bones; and calculate a point on the binary image of skin which is nearest to the center of mass; partition the CT image of the liver tissue or the MRI image of the liver tissue into four quadrants according to the center of mass and the point nearest to the center of mass; fit each rib point in a second quadrant to obtain a rib fitted curve; and move the rib fitted curve towards a first quadrant by a predefined value to obtain a boundary region curve; and determine a region between the boundary region curve and the rib fitted curve as a boundary region of the liver tissue.
 16. The system according to claim 11, wherein the processor is further configured to: output a coordinate position of the identified liver tissue boundary; and/or display an image of the identified liver tissue boundary.
 17. The system according to claim 12, wherein the processor is further configured to: output a coordinate position of the identified liver tissue boundary; and/or display an image of the identified liver tissue boundary.
 18. The system according to claim 13, wherein the processor is further configured to: output a coordinate position of the identified liver tissue boundary; and/or display an image of the identified liver tissue boundary.
 19. The system according to claim 14, wherein the processor is further configured to: output a coordinate position of the identified liver tissue boundary; and/or display an image of the identified liver tissue boundary.
 20. The system according to claim 15, wherein the processor is further configured to: output a coordinate position of the identified liver tissue boundary; and/or display an image of the identified liver tissue boundary. 